
# !pip install git+https://github.com/alberanid/imdbpy
# !pip install pandas
# !pip install numpy
# !pip install matplotlib
# !pip install seaborn
# !pip install pandas_profiling --upgrade
# !pip install plotly
# !pip install wordcloud
# !pip install Flask


# Import Dataset
# Import File from Loacal Drive
# from google.colab import files
# data_to_load = files.upload()
# from google.colab import drive
# drive.mount('/content/drive')


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import collections
import plotly.express as px
import plotly.graph_objects as go
import nltk
import re
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.probability import FreqDist
from nltk.util import ngrams
from plotly.subplots import make_subplots
from plotly.offline import iplot, init_notebook_mode
from wordcloud import WordCloud, STOPWORDS
from pandas_profiling import ProfileReport
%matplotlib inline
warnings.filterwarnings("ignore")


nltk.download('all')

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[nltk_data]    | Downloading package wmt15_eval to
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[nltk_data]    |   Package wmt15_eval is already up-to-date!
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[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
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True


# path = '/content/drive/MyDrive/Files/'

path = 'C:\\Users\\pawan\\OneDrive\\Desktop\\ott\\Data\\'
 
df_movies = pd.read_csv(path + 'ottmovies.csv')
 
df_movies.head()


# profile = ProfileReport(df_movies)
# profile


def data_investigate(df):
    print('No of Rows : ', df.shape[0])
    print('No of Coloums : ', df.shape[1])
    print('**'*25)
    print('Colums Names : \n', df.columns)
    print('**'*25)
    print('Datatype of Columns : \n', df.dtypes)
    print('**'*25)
    print('Missing Values : ')
    c = df.isnull().sum()
    c = c[c > 0]
    print(c)
    print('**'*25)
    print('Missing vaules %age wise :\n')
    print((100*(df.isnull().sum()/len(df.index))))
    print('**'*25)
    print('Pictorial Representation : ')
    plt.figure(figsize = (10, 10))
    sns.heatmap(df.isnull(), yticklabels = False, cbar = False)
    plt.show()


data_investigate(df_movies)

No of Rows :  16923
No of Coloums :  20
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb               float64
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime            float64
Kind                object
Seasons            float64
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
dtype: object
**************************************************
Missing Values : 
Age                 8457
IMDb                 328
Rotten Tomatoes    10437
Directors            357
Cast                 648
Genres               234
Country              303
Language             437
Plotline            4958
Runtime              382
Seasons            16923
dtype: int64
**************************************************
Missing vaules %age wise :

ID                   0.000000
Title                0.000000
Year                 0.000000
Age                 49.973409
IMDb                 1.938191
Rotten Tomatoes     61.673462
Directors            2.109555
Cast                 3.829108
Genres               1.382734
Country              1.790463
Language             2.582284
Plotline            29.297406
Runtime              2.257283
Kind                 0.000000
Seasons            100.000000
Netflix              0.000000
Hulu                 0.000000
Prime Video          0.000000
Disney+              0.000000
Type                 0.000000
dtype: float64
**************************************************
Pictorial Representation :


# ID
# df_movies = df_movies.drop(['ID'], axis = 1)
 
# Age
df_movies.loc[df_movies['Age'].isnull() & df_movies['Disney+'] == 1, "Age"] = '13'
# df_movies.fillna({'Age' : 18}, inplace = True)
df_movies.fillna({'Age' : 'NR'}, inplace = True)
df_movies['Age'].replace({'all': '0'}, inplace = True)
df_movies['Age'].replace({'7+': '7'}, inplace = True)
df_movies['Age'].replace({'13+': '13'}, inplace = True)
df_movies['Age'].replace({'16+': '16'}, inplace = True)
df_movies['Age'].replace({'18+': '18'}, inplace = True)
# df_movies['Age'] = df_movies['Age'].astype(int)
 
# IMDb
# df_movies.fillna({'IMDb' : df_movies['IMDb'].mean()}, inplace = True)
# df_movies.fillna({'IMDb' : df_movies['IMDb'].median()}, inplace = True)
df_movies.fillna({'IMDb' : "NA"}, inplace = True)
 
# Rotten Tomatoes
df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'][df_movies['Rotten Tomatoes'].notnull()].str.replace('%', '').astype(int)
# df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'][df_movies['Rotten Tomatoes'].notnull()].astype(int)
# df_movies.fillna({'Rotten Tomatoes' : df_movies['Rotten Tomatoes'].mean()}, inplace = True)
# df_movies.fillna({'Rotten Tomatoes' : df_movies['Rotten Tomatoes'].median()}, inplace = True)
# df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'].astype(int)
df_movies.fillna({'Rotten Tomatoes' : "NA"}, inplace = True)
 
# Directors
# df_movies = df_movies.drop(['Directors'], axis = 1)
df_movies.fillna({'Directors' : "NA"}, inplace = True)
 
# Cast
df_movies.fillna({'Cast' : "NA"}, inplace = True)
 
# Genres
df_movies.fillna({'Genres': "NA"}, inplace = True)
 
# Country
df_movies.fillna({'Country': "NA"}, inplace = True)
 
# Language
df_movies.fillna({'Language': "NA"}, inplace = True)
 
# Plotline
df_movies.fillna({'Plotline': "NA"}, inplace = True)
 
# Runtime
# df_movies.fillna({'Runtime' : df_movies['Runtime'].mean()}, inplace = True)
# df_movies['Runtime'] = df_movies['Runtime'].astype(int)
df_movies.fillna({'Runtime' : "NA"}, inplace = True)
 
# Kind
# df_movies.fillna({'Kind': "NA"}, inplace = True)
 
# Type
# df_movies.fillna({'Type': "NA"}, inplace = True)
# df_movies = df_movies.drop(['Type'], axis = 1)
 
# Seasons
# df_movies.fillna({'Seasons': 1}, inplace = True)
# df_movies.fillna({'Seasons': "NA"}, inplace = True)
df_movies = df_movies.drop(['Seasons'], axis = 1)
# df_movies['Seasons'] = df_movies['Seasons'].astype(int)
# df_movies.fillna({'Seasons' : df_movies['Seasons'].mean()}, inplace = True)
# df_movies['Seasons'] = df_movies['Seasons'].astype(int)
 
# Service Provider
df_movies['Service Provider'] = df_movies.loc[:, ['Netflix', 'Prime Video', 'Disney+', 'Hulu']].idxmax(axis = 1)
# df_movies.drop(['Netflix','Prime Video','Disney+','Hulu'], axis = 1)

# Removing Duplicate and Missing Entries
df_movies.dropna(how = 'any', inplace = True)
df_movies.drop_duplicates(inplace = True)


data_investigate(df_movies)

No of Rows :  16923
No of Coloums :  20
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type',
       'Service Provider'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb                object
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime             object
Kind                object
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
Service Provider    object
dtype: object
**************************************************
Missing Values : 
Series([], dtype: int64)
**************************************************
Missing vaules %age wise :

ID                  0.0
Title               0.0
Year                0.0
Age                 0.0
IMDb                0.0
Rotten Tomatoes     0.0
Directors           0.0
Cast                0.0
Genres              0.0
Country             0.0
Language            0.0
Plotline            0.0
Runtime             0.0
Kind                0.0
Netflix             0.0
Hulu                0.0
Prime Video         0.0
Disney+             0.0
Type                0.0
Service Provider    0.0
dtype: float64
**************************************************
Pictorial Representation :


df_movies.head()


df_movies.describe()


df_movies.corr()


# df_movies.sort_values('Year', ascending = True)
# df_movies.sort_values('IMDb', ascending = False)


# df_movies.to_csv(path_or_buf= '/content/drive/MyDrive/Files/updated_ottmovies.csv', index = False)
 
# path = '/content/drive/MyDrive/Files/'
 
# udf_movies = pd.read_csv(path + 'updated_ottmovies.csv')
 
# udf_movies


# df_netflix_movies = df_movies.loc[(df_movies['Netflix'] > 0)]
# df_hulu_movies = df_movies.loc[(df_movies['Hulu'] > 0)]
# df_prime_video_movies = df_movies.loc[(df_movies['Prime Video'] > 0)]
# df_disney_movies = df_movies.loc[(df_movies['Disney+'] > 0)]


df_netflix_only_movies = df_movies[(df_movies['Netflix'] == 1) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 0)]
df_hulu_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 1) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 0)]
df_prime_video_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 1 ) & (df_movies['Disney+'] == 0)]
df_disney_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 1)]


df_movies_genres = df_movies.copy()


df_movies_genres.drop(df_movies_genres.loc[df_movies_genres['Genres'] == "NA"].index, inplace = True)
# df_movies_genres = df_movies_genres[df_movies_genres.Genre != "NA"]
# df_movies_genres['Genres'] = df_movies_genres['Genres'].astype(str)


df_movies_count_genres = df_movies_genres.copy()


df_movies_genre = df_movies_genres.copy()


# Create genres dict where key=name and value = number of genres
 
genres = {}
 
for i in df_movies_count_genres['Genres'].dropna():
    if i != "NA":
        #print(i,len(i.split(',')))
        genres[i] = len(i.split(','))
    else:
        genres[i] = 0
    
# Add this information to our dataframe as a new column
 
df_movies_count_genres['Number of Genres'] = df_movies_count_genres['Genres'].map(genres).astype(int)


df_movies_mixed_genres = df_movies_count_genres.copy()


# Creating distinct dataframes only with the movies present on individual streaming platforms
netflix_genres_movies = df_movies_count_genres.loc[df_movies_count_genres['Netflix'] == 1]
hulu_genres_movies = df_movies_count_genres.loc[df_movies_count_genres['Hulu'] == 1]
prime_video_genres_movies = df_movies_count_genres.loc[df_movies_count_genres['Prime Video'] == 1]
disney_genres_movies = df_movies_count_genres.loc[df_movies_count_genres['Disney+'] == 1]


plt.figure(figsize = (10, 10))
corr = df_movies_count_genres.corr()
# Plot figsize
fig, ax = plt.subplots(figsize=(10, 8))
# Generate Heat Map, alleast annotations and place floats in map
sns.heatmap(corr, cmap = 'magma', annot = True, fmt = ".2f")
# Apply xticks
plt.xticks(range(len(corr.columns)), corr.columns);
# Apply yticks
plt.yticks(range(len(corr.columns)), corr.columns)
# show plot
plt.show()
fig.show()

<Figure size 720x720 with 0 Axes>


df_genres_most_movies = df_movies_count_genres.sort_values(by = 'Number of Genres', ascending = False).reset_index()
df_genres_most_movies = df_genres_most_movies.drop(['index'], axis = 1)
# filter = (df_movies_count_genres['Number of Genres'] == (df_movies_count_genres['Number of Genres'].max()))
# df_genres_most_movies = df_movies_count_genres[filter]
 
# mostest_rated_movies = df_movies_count_genres.loc[df_movies_count_genres['Number of Genres'].idxmax()]
 
print('\nMovies with Highest Ever Number of Genres are : \n')
df_genres_most_movies.head(5)

Movies with Highest Ever Number of Genres are :


fig = px.bar(y = df_genres_most_movies['Title'][:15],
             x = df_genres_most_movies['Number of Genres'][:15], 
             color = df_genres_most_movies['Number of Genres'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Genres'},
             title  = 'Movies with Highest Number of Genres : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


df_genres_least_movies = df_movies_count_genres.sort_values(by = 'Number of Genres', ascending = True).reset_index()
df_genres_least_movies = df_genres_least_movies.drop(['index'], axis = 1)
# filter = (df_movies_count_genres['Number of Genres'] == (df_movies_count_genres['Number of Genres'].min()))
# df_genres_least_movies = df_movies_count_genres[filter]

print('\nMovies with Lowest Ever Number of Genres are : \n')
df_genres_least_movies.head(5)

Movies with Lowest Ever Number of Genres are :


fig = px.bar(y = df_genres_least_movies['Title'][:15],
             x = df_genres_least_movies['Number of Genres'][:15], 
             color = df_genres_least_movies['Number of Genres'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Genres'},
             title  = 'Movies with Lowest Number of Genres : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


print(f'''
      Total '{df_movies_count_genres['Number of Genres'].unique().shape[0]}' unique Number of Genres s were Given, They were Like this,\n
      
      {df_movies_count_genres.sort_values(by = 'Number of Genres', ascending = False)['Number of Genres'].unique()}\n
 
      The Highest Number of Genres Ever Any Movie Got is '{df_genres_most_movies['Title'][0]}' : '{df_genres_most_movies['Number of Genres'].max()}'\n
 
      The Lowest Number of Genres Ever Any Movie Got is '{df_genres_least_movies['Title'][0]}' : '{df_genres_least_movies['Number of Genres'].min()}'\n
      ''')

      Total '9' unique Number of Genres s were Given, They were Like this,

      
      [9 8 7 6 5 4 3 2 1]

 
      The Highest Number of Genres Ever Any Movie Got is 'Gappa, the Triphibian Monster' : '9'

 
      The Lowest Number of Genres Ever Any Movie Got is 'Mark Of The Witch' : '1'


netflix_genres_most_movies = df_genres_most_movies.loc[df_genres_most_movies['Netflix']==1].reset_index()
netflix_genres_most_movies = netflix_genres_most_movies.drop(['index'], axis = 1)
 
netflix_genres_least_movies = df_genres_least_movies.loc[df_genres_least_movies['Netflix']==1].reset_index()
netflix_genres_least_movies = netflix_genres_least_movies.drop(['index'], axis = 1)
 
netflix_genres_most_movies.head(5)


fig = px.bar(y = netflix_genres_most_movies['Title'][:15],
             x = netflix_genres_most_movies['Number of Genres'][:15], 
             color = netflix_genres_most_movies['Number of Genres'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Genres'},
             title  = 'Movies with Highest Number of Genres : Netflix')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


fig = px.bar(y = netflix_genres_least_movies['Title'][:15],
             x = netflix_genres_least_movies['Number of Genres'][:15], 
             color = netflix_genres_least_movies['Number of Genres'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Genres'},
             title  = 'Movies with Lowest Number of Genres : Netflix')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


hulu_genres_most_movies = df_genres_most_movies.loc[df_genres_most_movies['Hulu']==1].reset_index()
hulu_genres_most_movies = hulu_genres_most_movies.drop(['index'], axis = 1)
 
hulu_genres_least_movies = df_genres_least_movies.loc[df_genres_least_movies['Hulu']==1].reset_index()
hulu_genres_least_movies = hulu_genres_least_movies.drop(['index'], axis = 1)
 
hulu_genres_most_movies.head(5)


fig = px.bar(y = hulu_genres_most_movies['Title'][:15],
             x = hulu_genres_most_movies['Number of Genres'][:15], 
             color = hulu_genres_most_movies['Number of Genres'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Genres'},
             title  = 'Movies with Highest Number of Genres : Hulu')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


fig = px.bar(y = hulu_genres_least_movies['Title'][:15],
             x = hulu_genres_least_movies['Number of Genres'][:15], 
             color = hulu_genres_least_movies['Number of Genres'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Genres'},
             title  = 'Movies with Lowest Number of Genres : Hulu')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


prime_video_genres_most_movies = df_genres_most_movies.loc[df_genres_most_movies['Prime Video']==1].reset_index()
prime_video_genres_most_movies = prime_video_genres_most_movies.drop(['index'], axis = 1)
 
prime_video_genres_least_movies = df_genres_least_movies.loc[df_genres_least_movies['Prime Video']==1].reset_index()
prime_video_genres_least_movies = prime_video_genres_least_movies.drop(['index'], axis = 1)
 
prime_video_genres_most_movies.head(5)


fig = px.bar(y = prime_video_genres_most_movies['Title'][:15],
             x = prime_video_genres_most_movies['Number of Genres'][:15], 
             color = prime_video_genres_most_movies['Number of Genres'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Genres'},
             title  = 'Movies with Highest Number of Genres : Prime Video')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


fig = px.bar(y = prime_video_genres_least_movies['Title'][:15],
             x = prime_video_genres_least_movies['Number of Genres'][:15], 
             color = prime_video_genres_least_movies['Number of Genres'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Genres'},
             title  = 'Movies with Lowest Number of Genres : Prime Video')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


disney_genres_most_movies = df_genres_most_movies.loc[df_genres_most_movies['Disney+']==1].reset_index()
disney_genres_most_movies = disney_genres_most_movies.drop(['index'], axis = 1)
 
disney_genres_least_movies = df_genres_least_movies.loc[df_genres_least_movies['Disney+']==1].reset_index()
disney_genres_least_movies = disney_genres_least_movies.drop(['index'], axis = 1)
 
disney_genres_most_movies.head(5)


fig = px.bar(y = disney_genres_most_movies['Title'][:15],
             x = disney_genres_most_movies['Number of Genres'][:15], 
             color = disney_genres_most_movies['Number of Genres'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Genres'},
             title  = 'Movies with Highest Number of Genres : Disney+')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


fig = px.bar(y = disney_genres_least_movies['Title'][:15],
             x = disney_genres_least_movies['Number of Genres'][:15], 
             color = disney_genres_least_movies['Number of Genres'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Genres'},
             title  = 'Movies with Lowest Number of Genres : Disney+')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


print(f'''
      The Movie with Highest Number of Genres Ever Got is '{df_genres_most_movies['Title'][0]}' : '{df_genres_most_movies['Number of Genres'].max()}'\n
      The Movie with Lowest Number of Genres Ever Got is '{df_genres_least_movies['Title'][0]}' : '{df_genres_least_movies['Number of Genres'].min()}'\n
      
      The Movie with Highest Number of Genres on 'Netflix' is '{netflix_genres_most_movies['Title'][0]}' : '{netflix_genres_most_movies['Number of Genres'].max()}'\n
      The Movie with Lowest Number of Genres on 'Netflix' is '{netflix_genres_least_movies['Title'][0]}' : '{netflix_genres_least_movies['Number of Genres'].min()}'\n
      
      The Movie with Highest Number of Genres on 'Hulu' is '{hulu_genres_most_movies['Title'][0]}' : '{hulu_genres_most_movies['Number of Genres'].max()}'\n
      The Movie with Lowest Number of Genres on 'Hulu' is '{hulu_genres_least_movies['Title'][0]}' : '{hulu_genres_least_movies['Number of Genres'].min()}'\n
      
      The Movie with Highest Number of Genres on 'Prime Video' is '{prime_video_genres_most_movies['Title'][0]}' : '{prime_video_genres_most_movies['Number of Genres'].max()}'\n
      The Movie with Lowest Number of Genres on 'Prime Video' is '{prime_video_genres_least_movies['Title'][0]}' : '{prime_video_genres_least_movies['Number of Genres'].min()}'\n
      
      The Movie with Highest Number of Genres on 'Disney+' is '{disney_genres_most_movies['Title'][0]}' : '{disney_genres_most_movies['Number of Genres'].max()}'\n
      The Movie with Lowest Number of Genres on 'Disney+' is '{disney_genres_least_movies['Title'][0]}' : '{disney_genres_least_movies['Number of Genres'].min()}'\n 
      ''')

      The Movie with Highest Number of Genres Ever Got is 'Gappa, the Triphibian Monster' : '9'

      The Movie with Lowest Number of Genres Ever Got is 'Mark Of The Witch' : '1'

      
      The Movie with Highest Number of Genres on 'Netflix' is 'WHAT DID JACK DO?' : '8'

      The Movie with Lowest Number of Genres on 'Netflix' is 'Bangkok รัก Stories 2 ตอน ไม่เดียงสา' : '1'

      
      The Movie with Highest Number of Genres on 'Hulu' is 'The Osiris Child' : '7'

      The Movie with Lowest Number of Genres on 'Hulu' is 'In the Radiant City' : '1'

      
      The Movie with Highest Number of Genres on 'Prime Video' is 'Gappa, the Triphibian Monster' : '9'

      The Movie with Lowest Number of Genres on 'Prime Video' is 'Mark Of The Witch' : '1'

      
      The Movie with Highest Number of Genres on 'Disney+' is 'The Wizards Return: Alex vs. Alex' : '8'

      The Movie with Lowest Number of Genres on 'Disney+' is 'Man Among Cheetahs' : '1'


print(f'''
      Accross All Platforms the Average Number of Genres is '{round(df_movies_count_genres['Number of Genres'].mean(), ndigits = 2)}'\n
      The Average Number of Genres on 'Netflix' is '{round(netflix_genres_movies['Number of Genres'].mean(), ndigits = 2)}'\n
      The Average Number of Genres on 'Hulu' is '{round(hulu_genres_movies['Number of Genres'].mean(), ndigits = 2)}'\n
      The Average Number of Genres on 'Prime Video' is '{round(prime_video_genres_movies['Number of Genres'].mean(), ndigits = 2)}'\n
      The Average Number of Genres on 'Disney+' is '{round(disney_genres_movies['Number of Genres'].mean(), ndigits = 2)}'\n 
      ''')

      Accross All Platforms the Average Number of Genres is '2.41'

      The Average Number of Genres on 'Netflix' is '2.43'

      The Average Number of Genres on 'Hulu' is '2.64'

      The Average Number of Genres on 'Prime Video' is '2.33'

      The Average Number of Genres on 'Disney+' is '3.73'


print(f'''
      Accross All Platforms Total Count of Genre is '{df_movies_count_genres['Number of Genres'].max()}'\n
      Total Count of Genre on 'Netflix' is '{netflix_genres_movies['Number of Genres'].max()}'\n
      Total Count of Genre on 'Hulu' is '{hulu_genres_movies['Number of Genres'].max()}'\n
      Total Count of Genre on 'Prime Video' is '{prime_video_genres_movies['Number of Genres'].max()}'\n
      Total Count of Genre on 'Disney+' is '{disney_genres_movies['Number of Genres'].max()}'\n 
      ''')

      Accross All Platforms Total Count of Genre is '9'

      Total Count of Genre on 'Netflix' is '8'

      Total Count of Genre on 'Hulu' is '7'

      Total Count of Genre on 'Prime Video' is '9'

      Total Count of Genre on 'Disney+' is '8'


f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(df_movies_count_genres['Number of Genres'],bins = 20, kde = True, ax = ax[0])
sns.boxplot(df_movies_count_genres['Number of Genres'], ax = ax[1])
plt.show()


# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Number of Genres s Per Platform')
 
# Plotting the information from each dataset into a histogram
sns.histplot(prime_video_genres_movies['Number of Genres'], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_genres_movies['Number of Genres'], color = 'red', legend = True, kde = True)
sns.histplot(hulu_genres_movies['Number of Genres'], color = 'lightgreen', legend = True, kde = True)
sns.histplot(disney_genres_movies['Number of Genres'], color = 'darkblue', legend = True, kde = True) 
 
# Setting the legend
plt.legend(['Prime Video', 'Netflix', 'Hulu', 'Disney+'])
plt.show()


df_lan = df_movies_genre['Genres'].str.split(',').apply(pd.Series).stack()
del df_movies_genre['Genres']
df_lan.index = df_lan.index.droplevel(-1)
df_lan.name = 'Genre'
df_movies_genre = df_movies_genre.join(df_lan)
df_movies_genre.drop_duplicates(inplace = True)


df_movies_genre.head(5)


genre_count = df_movies_genre.groupby('Genre')['Title'].count()
genre_movies = df_movies_genre.groupby('Genre')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
genre_data_movies = pd.concat([genre_count, genre_movies], axis = 1).reset_index().rename(columns = {'Title' : 'Movies Count'})
genre_data_movies = genre_data_movies.sort_values(by = 'Movies Count', ascending = False)


# Creating distinct dataframes only with the movies present on individual streaming platforms
netflix_genre_movies = genre_data_movies[genre_data_movies['Netflix'] !=  0].sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_genre_movies = netflix_genre_movies.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)

hulu_genre_movies = genre_data_movies[genre_data_movies['Hulu'] !=  0].sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_genre_movies = hulu_genre_movies.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)

prime_video_genre_movies = genre_data_movies[genre_data_movies['Prime Video'] !=  0].sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_genre_movies = prime_video_genre_movies.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'Movies Count'], axis = 1)

disney_genre_movies = genre_data_movies[genre_data_movies['Disney+'] !=  0].sort_values(by = 'Disney+', ascending = False).reset_index()
disney_genre_movies = disney_genre_movies.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'Movies Count'], axis = 1)


# Genre with Movies Counts - All Platforms Combined
genre_data_movies.sort_values(by = 'Movies Count', ascending = False)[:10]


fig = px.bar(x = genre_data_movies['Genre'][:50],
             y = genre_data_movies['Movies Count'][:50], 
             color = genre_data_movies['Movies Count'][:50],
             color_continuous_scale = 'Teal_r', 
             labels = { 'x' : 'Genre', 'y' : 'Movies Count'},
             title  = 'Major Genres : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


df_genre_high_movies = genre_data_movies.sort_values(by = 'Movies Count', ascending = False).reset_index()
df_genre_high_movies = df_genre_high_movies.drop(['index'], axis = 1)
# filter = (genre_data_movies['Movies Count'] == (genre_data_movies['Movies Count'].max()))
# df_genre_high_movies = genre_data_movies[filter]
 
# highest_rated_movies = genre_data_movies.loc[genre_data_movies['Movies Count'].idxmax()]
 
print('\nGenre with Highest Ever Movies Count are : All Platforms Combined\n')
df_genre_high_movies.head(5)

Genre with Highest Ever Movies Count are : All Platforms Combined


fig = px.bar(y = df_genre_high_movies['Genre'][:15],
             x = df_genre_high_movies['Movies Count'][:15], 
             color = df_genre_high_movies['Movies Count'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Genre', 'x' : 'Movies Count'},
             title  = 'Genre with Highest Movies : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


df_genre_low_movies = genre_data_movies.sort_values(by = 'Movies Count', ascending = True).reset_index()
df_genre_low_movies = df_genre_low_movies.drop(['index'], axis = 1)
# filter = (genre_data_movies['Movies Count'] == (genre_data_movies['Movies Count'].min()))
# df_genre_low_movies = genre_data_movies[filter]

print('\nGenre with Lowest Ever Movies Count are : All Platforms Combined\n')
df_genre_low_movies.head(5)

Genre with Lowest Ever Movies Count are : All Platforms Combined


fig = px.bar(y = df_genre_low_movies['Genre'][:15],
             x = df_genre_low_movies['Movies Count'][:15], 
             color = df_genre_low_movies['Movies Count'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Genre', 'x' : 'Movies Count'},
             title  = 'Genre with Lowest Movies Count : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


print(f'''
      Total '{genre_data_movies['Genre'].unique().shape[0]}' unique Genre Count s were Given, They were Like this,\n
      
      {genre_data_movies.sort_values(by = 'Movies Count', ascending = False)['Genre'].unique()[:5]}\n
 
      The Highest Ever Movies Count Ever Any Movie Got is '{df_genre_high_movies['Genre'][0]}' : '{df_genre_high_movies['Movies Count'].max()}'\n
 
      The Lowest Ever Movies Count Ever Any Movie Got is '{df_genre_low_movies['Genre'][0]}' : '{df_genre_low_movies['Movies Count'].min()}'\n
      ''')

      Total '26' unique Genre Count s were Given, They were Like this,

      
      ['Drama' 'Comedy' 'Thriller' 'Action' 'Documentary']

 
      The Highest Ever Movies Count Ever Any Movie Got is 'Drama' : '7381'

 
      The Lowest Ever Movies Count Ever Any Movie Got is 'Talk-Show' : '2'


fig = px.pie(genre_data_movies[:10], names = 'Genre', values = 'Movies Count', color_discrete_sequence = px.colors.sequential.Teal)
fig.update_traces(textposition = 'inside', textinfo = 'percent+label', title = 'Movies Count based on Genre')
fig.show()


# netflix_genre_movies = genre_data_movies[genre_data_movies['Netflix'] !=  0].sort_values(by = 'Netflix', ascending = False).reset_index()
# netflix_genre_movies = netflix_genre_movies.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
 
netflix_genre_high_movies = df_genre_high_movies.sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_genre_high_movies = netflix_genre_high_movies.drop(['index'], axis = 1)
 
netflix_genre_low_movies = df_genre_high_movies.sort_values(by = 'Netflix', ascending = True).reset_index()
netflix_genre_low_movies = netflix_genre_low_movies.drop(['index'], axis = 1)
 
netflix_genre_high_movies.head(5)


fig = px.bar(x = netflix_genre_high_movies['Genre'][:15],
             y = netflix_genre_high_movies['Netflix'][:15], 
             color = netflix_genre_high_movies['Netflix'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Genre', 'x' : 'Movies Count'},
             title  = 'Genre with Highest Movies : Netflix')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


# hulu_genre_movies = genre_data_movies[genre_data_movies['Hulu'] !=  0].sort_values(by = 'Hulu', ascending = False).reset_index()
# hulu_genre_movies = hulu_genre_movies.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
 
hulu_genre_high_movies = df_genre_high_movies.sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_genre_high_movies = hulu_genre_high_movies.drop(['index'], axis = 1)
 
hulu_genre_low_movies = df_genre_high_movies.sort_values(by = 'Hulu', ascending = True).reset_index()
hulu_genre_low_movies = hulu_genre_low_movies.drop(['index'], axis = 1)
 
hulu_genre_high_movies.head(5)


fig = px.bar(x = hulu_genre_high_movies['Genre'][:15],
             y = hulu_genre_high_movies['Hulu'][:15], 
             color = hulu_genre_high_movies['Hulu'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Genre', 'x' : 'Movies Count'},
             title  = 'Genre with Highest Movies : Hulu')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


# prime_video_genre_movies = genre_data_movies[genre_data_movies['Prime Video'] !=  0].sort_values(by = 'Prime Video', ascending = False).reset_index()
# prime_video_genre_movies = prime_video_genre_movies.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'Movies Count'], axis = 1)
 
prime_video_genre_high_movies = df_genre_high_movies.sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_genre_high_movies = prime_video_genre_high_movies.drop(['index'], axis = 1)
 
prime_video_genre_low_movies = df_genre_high_movies.sort_values(by = 'Prime Video', ascending = True).reset_index()
prime_video_genre_low_movies = prime_video_genre_low_movies.drop(['index'], axis = 1)
 
prime_video_genre_high_movies.head(5)


fig = px.bar(x = prime_video_genre_high_movies['Genre'][:15],
             y = prime_video_genre_high_movies['Prime Video'][:15], 
             color = prime_video_genre_high_movies['Prime Video'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Genre', 'x' : 'Movies Count'},
             title  = 'Genre with Highest Movies : Prime Video')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


# disney_genre_movies = genre_data_movies[genre_data_movies['Disney+'] !=  0].sort_values(by = 'Disney+', ascending = False).reset_index()
# disney_genre_movies = disney_genre_movies.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'Movies Count'], axis = 1)
 
disney_genre_high_movies = df_genre_high_movies.sort_values(by = 'Disney+', ascending = False).reset_index()
disney_genre_high_movies = disney_genre_high_movies.drop(['index'], axis = 1)
 
disney_genre_low_movies = df_genre_high_movies.sort_values(by = 'Disney+', ascending = True).reset_index()
disney_genre_low_movies = disney_genre_low_movies.drop(['index'], axis = 1)
 
disney_genre_high_movies.head(5)


fig = px.bar(x = disney_genre_high_movies['Genre'][:15],
             y = disney_genre_high_movies['Disney+'][:15], 
             color = disney_genre_high_movies['Disney+'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Genre', 'x' : 'Movies Count'},
             title  = 'Genre with Highest Movies : Disney+')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(genre_data_movies['Movies Count'], bins = 20, kde = True, ax = ax[0])
sns.boxplot(genre_data_movies['Movies Count'], ax = ax[1])
plt.show()


# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Genre Movies Count Per Platform')
 
# Plotting the information from each dataset into a histogram
 
sns.histplot(disney_genre_movies['Disney+'][:50], color = 'darkblue', legend = True, kde = True)  
sns.histplot(prime_video_genre_movies['Prime Video'][:50], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_genre_movies['Netflix'][:50], color = 'red', legend = True, kde = True)
sns.histplot(hulu_genre_movies['Hulu'][:50], color = 'lightgreen', legend = True, kde = True)                                
 
# Setting the legend
plt.legend(['Disney+', 'Prime Video', 'Netflix', 'Hulu'])
plt.show()


print(f'''
      The Genre with Highest Movies Count Ever Got is '{df_genre_high_movies['Genre'][0]}' : '{df_genre_high_movies['Movies Count'].max()}'\n
      The Genre with Lowest Movies Count Ever Got is '{df_genre_low_movies['Genre'][0]}' : '{df_genre_low_movies['Movies Count'].min()}'\n
      
      The Genre with Highest Movies Count on 'Netflix' is '{netflix_genre_high_movies['Genre'][0]}' : '{netflix_genre_high_movies['Netflix'].max()}'\n
      The Genre with Lowest Movies Count on 'Netflix' is '{netflix_genre_low_movies['Genre'][0]}' : '{netflix_genre_low_movies['Netflix'].min()}'\n
      
      The Genre with Highest Movies Count on 'Hulu' is '{hulu_genre_high_movies['Genre'][0]}' : '{hulu_genre_high_movies['Hulu'].max()}'\n
      The Genre with Lowest Movies Count on 'Hulu' is '{hulu_genre_low_movies['Genre'][0]}' : '{hulu_genre_low_movies['Hulu'].min()}'\n
      
      The Genre with Highest Movies Count on 'Prime Video' is '{prime_video_genre_high_movies['Genre'][0]}' : '{prime_video_genre_high_movies['Prime Video'].max()}'\n
      The Genre with Lowest Movies Count on 'Prime Video' is '{prime_video_genre_low_movies['Genre'][0]}' : '{prime_video_genre_low_movies['Prime Video'].min()}'\n
      
      The Genre with Highest Movies Count on 'Disney+' is '{disney_genre_high_movies['Genre'][0]}' : '{disney_genre_high_movies['Disney+'].max()}'\n
      The Genre with Lowest Movies Count on 'Disney+' is '{disney_genre_low_movies['Genre'][0]}' : '{disney_genre_low_movies['Disney+'].min()}'\n 
      ''')

      The Genre with Highest Movies Count Ever Got is 'Drama' : '7381'

      The Genre with Lowest Movies Count Ever Got is 'Talk-Show' : '2'

      
      The Genre with Highest Movies Count on 'Netflix' is 'Drama' : '1592'

      The Genre with Lowest Movies Count on 'Netflix' is 'Talk-Show' : '0'

      
      The Genre with Highest Movies Count on 'Hulu' is 'Drama' : '511'

      The Genre with Lowest Movies Count on 'Hulu' is 'Talk-Show' : '0'

      
      The Genre with Highest Movies Count on 'Prime Video' is 'Drama' : '5436'

      The Genre with Lowest Movies Count on 'Prime Video' is 'Talk-Show' : '2'

      
      The Genre with Highest Movies Count on 'Disney+' is 'Family' : '414'

      The Genre with Lowest Movies Count on 'Disney+' is 'Talk-Show' : '0'


# Distribution of movies genre in each platform
plt.figure(figsize = (20, 5))
plt.title('Genre with Movies Count for All Platforms')
sns.violinplot(x = genre_data_movies['Movies Count'][:100], color = 'gold', legend = True, kde = True, shade = False)
plt.show()


# Distribution of Genre Movies Count in each platform
f1, ax1 = plt.subplots(1, 2 , figsize = (20, 5))
sns.violinplot(x = netflix_genre_movies['Netflix'][:100], color = 'red', ax = ax1[0])
sns.violinplot(x = hulu_genre_movies['Hulu'][:100], color = 'lightgreen', ax = ax1[1])
 
f2, ax2 = plt.subplots(1, 2 , figsize = (20, 5))
sns.violinplot(x = prime_video_genre_movies['Prime Video'][:100], color = 'lightblue', ax = ax2[0])
sns.violinplot(x = disney_genre_movies['Disney+'][:100], color = 'darkblue', ax = ax2[1])
plt.show()


print(f'''
      Accross All Platforms the Average Movies Count of Genre is '{round(genre_data_movies['Movies Count'].mean(), ndigits = 2)}'\n
      The Average Movies Count of Genre on 'Netflix' is '{round(netflix_genre_movies['Netflix'].mean(), ndigits = 2)}'\n
      The Average Movies Count of Genre on 'Hulu' is '{round(hulu_genre_movies['Hulu'].mean(), ndigits = 2)}'\n
      The Average Movies Count of Genre on 'Prime Video' is '{round(prime_video_genre_movies['Prime Video'].mean(), ndigits = 2)}'\n
      The Average Movies Count of Genre on 'Disney+' is '{round(disney_genre_movies['Disney+'].mean(), ndigits = 2)}'\n 
      ''')

      Accross All Platforms the Average Movies Count of Genre is '1545.54'

      The Average Movies Count of Genre on 'Netflix' is '347.2'

      The Average Movies Count of Genre on 'Hulu' is '111.16'

      The Average Movies Count of Genre on 'Prime Video' is '1089.58'

      The Average Movies Count of Genre on 'Disney+' is '87.12'


print(f'''
      Accross All Platforms Total Count of Genre is '{genre_data_movies['Genre'].unique().shape[0]}'\n
      Total Count of Genre on 'Netflix' is '{netflix_genre_movies['Genre'].unique().shape[0]}'\n
      Total Count of Genre on 'Hulu' is '{hulu_genre_movies['Genre'].unique().shape[0]}'\n
      Total Count of Genre on 'Prime Video' is '{prime_video_genre_movies['Genre'].unique().shape[0]}'\n
      Total Count of Genre on 'Disney+' is '{disney_genre_movies['Genre'].unique().shape[0]}'\n 
      ''')

      Accross All Platforms Total Count of Genre is '26'

      Total Count of Genre on 'Netflix' is '25'

      Total Count of Genre on 'Hulu' is '25'

      Total Count of Genre on 'Prime Video' is '26'

      Total Count of Genre on 'Disney+' is '24'


plt.figure(figsize = (20, 5))
sns.lineplot(x = genre_data_movies['Genre'][:10], y = genre_data_movies['Netflix'][:10], color = 'red')
sns.lineplot(x = genre_data_movies['Genre'][:10], y = genre_data_movies['Hulu'][:10], color = 'lightgreen')
sns.lineplot(x = genre_data_movies['Genre'][:10], y = genre_data_movies['Prime Video'][:10], color = 'lightblue')
sns.lineplot(x = genre_data_movies['Genre'][:10], y = genre_data_movies['Disney+'][:10], color = 'darkblue')
plt.xlabel('Genre', fontsize = 20)
plt.ylabel('Movies Count', fontsize = 20)
plt.show()


fig, axes = plt.subplots(2, 2, figsize = (20 , 10))
 
n_g_ax1 = sns.lineplot(y = genre_data_movies['Genre'][:10], x = genre_data_movies['Netflix'][:10], color = 'red', ax = axes[0, 0])
h_g_ax2 = sns.lineplot(y = genre_data_movies['Genre'][:10], x = genre_data_movies['Hulu'][:10], color = 'lightgreen', ax = axes[0, 1])
p_g_ax3 = sns.lineplot(y = genre_data_movies['Genre'][:10], x = genre_data_movies['Prime Video'][:10], color = 'lightblue', ax = axes[1, 0])
d_g_ax4 = sns.lineplot(y = genre_data_movies['Genre'][:10], x = genre_data_movies['Disney+'][:10], color = 'darkblue', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_g_ax1.title.set_text(labels[0])
h_g_ax2.title.set_text(labels[1])
p_g_ax3.title.set_text(labels[2])
d_g_ax4.title.set_text(labels[3])
 
plt.show()


fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_g_ax1 = sns.barplot(y = netflix_genre_movies['Genre'][:10], x = netflix_genre_movies['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_g_ax2 = sns.barplot(y = hulu_genre_movies['Genre'][:10], x = hulu_genre_movies['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_g_ax3 = sns.barplot(y = prime_video_genre_movies['Genre'][:10], x = prime_video_genre_movies['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_g_ax4 = sns.barplot(y = disney_genre_movies['Genre'][:10], x = disney_genre_movies['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_g_ax1.title.set_text(labels[0])
h_g_ax2.title.set_text(labels[1])
p_g_ax3.title.set_text(labels[2])
d_g_ax4.title.set_text(labels[3])
 
plt.show()


# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Genre  Movies Count Per Platform')
 
# Plotting the information from each dataset into a histogram
sns.kdeplot(netflix_genre_movies['Netflix'][:10], color = 'red', legend = True)
sns.kdeplot(hulu_genre_movies['Hulu'][:10], color = 'green', legend = True)
sns.kdeplot(prime_video_genre_movies['Prime Video'][:10], color = 'lightblue', legend = True)
sns.kdeplot(disney_genre_movies['Disney+'][:10], color = 'darkblue', legend = True)                                      
                                      
# Setting the legend
plt.legend(['Netflix', 'Hulu', 'Prime Video', 'Disney+'])
plt.show()


fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_g_ax1 = sns.barplot(y = genre_data_movies['Genre'][:10], x = genre_data_movies['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_g_ax2 = sns.barplot(y = genre_data_movies['Genre'][:10], x = genre_data_movies['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_g_ax3 = sns.barplot(y = genre_data_movies['Genre'][:10], x = genre_data_movies['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_g_ax4 = sns.barplot(y = genre_data_movies['Genre'][:10], x = genre_data_movies['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_g_ax1.title.set_text(labels[0])
h_g_ax2.title.set_text(labels[1])
p_g_ax3.title.set_text(labels[2])
d_g_ax4.title.set_text(labels[3])
 
plt.show()


df_movies_mixed_genres.drop(df_movies_mixed_genres.loc[df_movies_mixed_genres['Genres'] == "NA"].index, inplace = True)
# df_movies_mixed_genres = df_movies_mixed_genres[df_movies_mixed_genres.Genre != "NA"]
df_movies_mixed_genres.drop(df_movies_mixed_genres.loc[df_movies_mixed_genres['Number of Genres'] == 1].index, inplace = True)


df_movies_mixed_genres.head(5)


mixed_genres_count = df_movies_mixed_genres.groupby('Genres')['Title'].count()
mixed_genres_movies = df_movies_mixed_genres.groupby('Genres')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
mixed_genres_data_movies = pd.concat([mixed_genres_count, mixed_genres_movies], axis = 1).reset_index().rename(columns = {'Title' : 'Movies Count', 'Genres' : 'Mixed Genre'})
mixed_genres_data_movies = mixed_genres_data_movies.sort_values(by = 'Movies Count', ascending = False)


mixed_genres_data_movies.head(5)


# Mixed Genre with Movies Counts - All Platforms Combined
mixed_genres_data_movies.sort_values(by = 'Movies Count', ascending = False)[:10]


df_mixed_genres_high_movies = mixed_genres_data_movies.sort_values(by = 'Movies Count', ascending = False).reset_index()
df_mixed_genres_high_movies = df_mixed_genres_high_movies.drop(['index'], axis = 1)
# filter = (mixed_genres_data_movies['Movies Count'] = =  (mixed_genres_data_movies['Movies Count'].max()))
# df_mixed_genres_high_movies = mixed_genres_data_movies[filter]
 
# highest_rated_movies = mixed_genres_data_movies.loc[mixed_genres_data_movies['Movies Count'].idxmax()]
 
print('\nMixed Genre with Highest Ever Movies Count are : All Platforms Combined\n')
df_mixed_genres_high_movies.head(5)

Mixed Genre with Highest Ever Movies Count are : All Platforms Combined


fig = px.bar(y = df_mixed_genres_high_movies['Mixed Genre'][:15],
             x = df_mixed_genres_high_movies['Movies Count'][:15], 
             color = df_mixed_genres_high_movies['Movies Count'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Mixed Genre'},
             title  = 'Movies with Highest Number of Mixed Genres : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


df_mixed_genres_low_movies = mixed_genres_data_movies.sort_values(by = 'Movies Count', ascending = True).reset_index()
df_mixed_genres_low_movies = df_mixed_genres_low_movies.drop(['index'], axis = 1)
# filter = (mixed_genres_data_movies['Movies Count'] = =  (mixed_genres_data_movies['Movies Count'].min()))
# df_mixed_genres_low_movies = mixed_genres_data_movies[filter]
 
print('\nMixed Genre with Lowest Ever Movies Count are : All Platforms Combined\n')
df_mixed_genres_low_movies.head(5)

Mixed Genre with Lowest Ever Movies Count are : All Platforms Combined


fig = px.bar(y = df_mixed_genres_low_movies['Mixed Genre'][:15],
             x = df_mixed_genres_low_movies['Movies Count'][:15], 
             color = df_mixed_genres_low_movies['Movies Count'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Mixed Genre'},
             title  = 'Movies with Lowest Number of Mixed Genres : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()


print(f'''
      Total '{df_movies_genres['Genres'].count()}' Titles are available on All Platforms, out of which\n
      You Can Choose to see Movies from Total '{mixed_genres_data_movies['Mixed Genre'].unique().shape[0]}' Mixed Genre, They were Like this, \n
 
      {mixed_genres_data_movies.sort_values(by = 'Movies Count', ascending = False)['Mixed Genre'].head(5).unique()} etc. \n
 
      The Mixed Genre with Highest Movies Count have '{mixed_genres_data_movies['Movies Count'].max()}' Movies Available is '{df_mixed_genres_high_movies['Mixed Genre'][0]}', &\n
      The Mixed Genre with Lowest Movies Count have '{mixed_genres_data_movies['Movies Count'].min()}' Movies Available is '{df_mixed_genres_low_movies['Mixed Genre'][0]}'
      ''')

      Total '16689' Titles are available on All Platforms, out of which

      You Can Choose to see Movies from Total '1864' Mixed Genre, They were Like this, 

 
      ['Comedy,Drama' 'Drama,Romance' 'Comedy,Drama,Romance' 'Comedy,Romance'
 'Horror,Thriller'] etc. 

 
      The Mixed Genre with Highest Movies Count have '460' Movies Available is 'Comedy,Drama', &

      The Mixed Genre with Lowest Movies Count have '1' Movies Available is 'Action,Adventure,Crime,Film-Noir,Romance'


fig = px.pie(mixed_genres_data_movies[:10], names = 'Mixed Genre', values = 'Movies Count', color_discrete_sequence = px.colors.sequential.Teal)
fig.update_traces(textposition = 'inside', textinfo = 'percent+label', title = 'Movies Count based on Mixed Genre')
fig.show()


# netflix_mixed_genres_movies = mixed_genres_data_movies[mixed_genres_data_movies['Netflix'] !=  0].sort_values(by = 'Netflix', ascending = False).reset_index()
# netflix_mixed_genres_movies = netflix_mixed_genres_movies.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
 
netflix_mixed_genres_high_movies = df_mixed_genres_high_movies.sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_mixed_genres_high_movies = netflix_mixed_genres_high_movies.drop(['index'], axis = 1)
 
netflix_mixed_genres_low_movies = df_mixed_genres_high_movies.sort_values(by = 'Netflix', ascending = True).reset_index()
netflix_mixed_genres_low_movies = netflix_mixed_genres_low_movies.drop(['index'], axis = 1)
 
netflix_mixed_genres_high_movies.head(5)


# hulu_mixed_genres_movies = mixed_genres_data_movies[mixed_genres_data_movies['Hulu'] !=  0].sort_values(by = 'Hulu', ascending = False).reset_index()
# hulu_mixed_genres_movies = hulu_mixed_genres_movies.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
 
hulu_mixed_genres_high_movies = df_mixed_genres_high_movies.sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_mixed_genres_high_movies = hulu_mixed_genres_high_movies.drop(['index'], axis = 1)
 
hulu_mixed_genres_low_movies = df_mixed_genres_high_movies.sort_values(by = 'Hulu', ascending = True).reset_index()
hulu_mixed_genres_low_movies = hulu_mixed_genres_low_movies.drop(['index'], axis = 1)
 
hulu_mixed_genres_high_movies.head(5)


# prime_video_mixed_genres_movies = mixed_genres_data_movies[mixed_genres_data_movies['Prime Video'] !=  0].sort_values(by = 'Prime Video', ascending = False).reset_index()
# prime_video_mixed_genres_movies = prime_video_mixed_genres_movies.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'Movies Count'], axis = 1)
 
prime_video_mixed_genres_high_movies = df_mixed_genres_high_movies.sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_mixed_genres_high_movies = prime_video_mixed_genres_high_movies.drop(['index'], axis = 1)
 
prime_video_mixed_genres_low_movies = df_mixed_genres_high_movies.sort_values(by = 'Prime Video', ascending = True).reset_index()
prime_video_mixed_genres_low_movies = prime_video_mixed_genres_low_movies.drop(['index'], axis = 1)
 
prime_video_mixed_genres_high_movies.head(5)


# disney_mixed_genres_movies = mixed_genres_data_movies[mixed_genres_data_movies['Disney+'] !=  0].sort_values(by = 'Disney+', ascending = False).reset_index()
# disney_mixed_genres_movies = disney_mixed_genres_movies.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'Movies Count'], axis = 1)
 
disney_mixed_genres_high_movies = df_mixed_genres_high_movies.sort_values(by = 'Disney+', ascending = False).reset_index()
disney_mixed_genres_high_movies = disney_mixed_genres_high_movies.drop(['index'], axis = 1)
 
disney_mixed_genres_low_movies = df_mixed_genres_high_movies.sort_values(by = 'Disney+', ascending = True).reset_index()
disney_mixed_genres_low_movies = disney_mixed_genres_low_movies.drop(['index'], axis = 1)
 
disney_mixed_genres_high_movies.head(5)


f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(mixed_genres_data_movies['Movies Count'], bins = 20, kde = True, ax = ax[0])
sns.boxplot(mixed_genres_data_movies['Movies Count'], ax = ax[1])
plt.show()


# Creating distinct dataframes only with the movies present on individual streaming platforms
netflix_mixed_genres_movies = mixed_genres_data_movies[mixed_genres_data_movies['Netflix'] !=  0].sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_mixed_genres_movies = netflix_mixed_genres_movies.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)

hulu_mixed_genres_movies = mixed_genres_data_movies[mixed_genres_data_movies['Hulu'] !=  0].sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_mixed_genres_movies = hulu_mixed_genres_movies.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)

prime_video_mixed_genres_movies = mixed_genres_data_movies[mixed_genres_data_movies['Prime Video'] !=  0].sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_mixed_genres_movies = prime_video_mixed_genres_movies.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'Movies Count'], axis = 1)

disney_mixed_genres_movies = mixed_genres_data_movies[mixed_genres_data_movies['Disney+'] !=  0].sort_values(by = 'Disney+', ascending = False).reset_index()
disney_mixed_genres_movies = disney_mixed_genres_movies.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'Movies Count'], axis = 1)


# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Mixed Genre Movies Count Per Platform')
 
# Plotting the information from each dataset into a histogram
 
sns.histplot(prime_video_mixed_genres_movies['Prime Video'][:100], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_mixed_genres_movies['Netflix'][:100], color = 'red', legend = True, kde = True)
sns.histplot(hulu_mixed_genres_movies['Hulu'][:100], color = 'lightgreen', legend = True, kde = True)
sns.histplot(disney_mixed_genres_movies['Disney+'][:100], color = 'darkblue', legend = True, kde = True)                                
 
# Setting the legend
plt.legend(['Prime Video', 'Netflix', 'Hulu', 'Disney+'])
plt.show()


print(f'''
      The Mixed Genre with Highest Movies Count Ever Got is '{df_mixed_genres_high_movies['Mixed Genre'][0]}' : '{df_mixed_genres_high_movies['Movies Count'].max()}'\n
      The Mixed Genre with Lowest Movies Count Ever Got is '{df_mixed_genres_low_movies['Mixed Genre'][0]}' : '{df_mixed_genres_low_movies['Movies Count'].min()}'\n
      
      The Mixed Genre with Highest Movies Count on 'Netflix' is '{netflix_mixed_genres_high_movies['Mixed Genre'][0]}' : '{netflix_mixed_genres_high_movies['Netflix'].max()}'\n
      The Mixed Genre with Lowest Movies Count on 'Netflix' is '{netflix_mixed_genres_low_movies['Mixed Genre'][0]}' : '{netflix_mixed_genres_low_movies['Netflix'].min()}'\n
      
      The Mixed Genre with Highest Movies Count on 'Hulu' is '{hulu_mixed_genres_high_movies['Mixed Genre'][0]}' : '{hulu_mixed_genres_high_movies['Hulu'].max()}'\n
      The Mixed Genre with Lowest Movies Count on 'Hulu' is '{hulu_mixed_genres_low_movies['Mixed Genre'][0]}' : '{hulu_mixed_genres_low_movies['Hulu'].min()}'\n
      
      The Mixed Genre with Highest Movies Count on 'Prime Video' is '{prime_video_mixed_genres_high_movies['Mixed Genre'][0]}' : '{prime_video_mixed_genres_high_movies['Prime Video'].max()}'\n
      The Mixed Genre with Lowest Movies Count on 'Prime Video' is '{prime_video_mixed_genres_low_movies['Mixed Genre'][0]}' : '{prime_video_mixed_genres_low_movies['Prime Video'].min()}'\n
      
      The Mixed Genre with Highest Movies Count on 'Disney+' is '{disney_mixed_genres_high_movies['Mixed Genre'][0]}' : '{disney_mixed_genres_high_movies['Disney+'].max()}'\n
      The Mixed Genre with Lowest Movies Count on 'Disney+' is '{disney_mixed_genres_low_movies['Mixed Genre'][0]}' : '{disney_mixed_genres_low_movies['Disney+'].min()}'\n 
      ''')

      The Mixed Genre with Highest Movies Count Ever Got is 'Comedy,Drama' : '460'

      The Mixed Genre with Lowest Movies Count Ever Got is 'Action,Adventure,Crime,Film-Noir,Romance' : '1'

      
      The Mixed Genre with Highest Movies Count on 'Netflix' is 'Comedy,Drama' : '128'

      The Mixed Genre with Lowest Movies Count on 'Netflix' is 'Animation,Short,Drama,Western' : '0'

      
      The Mixed Genre with Highest Movies Count on 'Hulu' is 'Comedy,Drama' : '42'

      The Mixed Genre with Lowest Movies Count on 'Hulu' is 'Animation,Short,Drama,Western' : '0'

      
      The Mixed Genre with Highest Movies Count on 'Prime Video' is 'Comedy,Drama' : '315'

      The Mixed Genre with Lowest Movies Count on 'Prime Video' is 'Biography,Drama,Sport,Thriller' : '0'

      
      The Mixed Genre with Highest Movies Count on 'Disney+' is 'Action,Adventure,Sci-Fi' : '19'

      The Mixed Genre with Lowest Movies Count on 'Disney+' is 'Comedy,Drama' : '0'


print(f'''
      Accross All Platforms the Average Movies Count of Mixed Genre is '{round(mixed_genres_data_movies['Movies Count'].mean(), ndigits = 2)}'\n
      The Average Movies Count of Mixed Genre on 'Netflix' is '{round(netflix_mixed_genres_movies['Netflix'].mean(), ndigits = 2)}'\n
      The Average Movies Count of Mixed Genre on 'Hulu' is '{round(hulu_mixed_genres_movies['Hulu'].mean(), ndigits = 2)}'\n
      The Average Movies Count of Mixed Genre on 'Prime Video' is '{round(prime_video_mixed_genres_movies['Prime Video'].mean(), ndigits = 2)}'\n
      The Average Movies Count of Mixed Genre on 'Disney+' is '{round(disney_mixed_genres_movies['Disney+'].mean(), ndigits = 2)}'\n 
      ''')

      Accross All Platforms the Average Movies Count of Mixed Genre is '6.36'

      The Average Movies Count of Mixed Genre on 'Netflix' is '3.7'

      The Average Movies Count of Mixed Genre on 'Hulu' is '2.39'

      The Average Movies Count of Mixed Genre on 'Prime Video' is '5.72'

      The Average Movies Count of Mixed Genre on 'Disney+' is '1.9'


print(f'''
      Accross All Platforms Total Count of Mixed Genre is '{mixed_genres_data_movies['Mixed Genre'].unique().shape[0]}'\n
      Total Count of Mixed Genre on 'Netflix' is '{netflix_mixed_genres_movies['Mixed Genre'].unique().shape[0]}'\n
      Total Count of Mixed Genre on 'Hulu' is '{hulu_mixed_genres_movies['Mixed Genre'].unique().shape[0]}'\n
      Total Count of Mixed Genre on 'Prime Video' is '{prime_video_mixed_genres_movies['Mixed Genre'].unique().shape[0]}'\n
      Total Count of Mixed Genre on 'Disney+' is '{disney_mixed_genres_movies['Mixed Genre'].unique().shape[0]}'\n 
      ''')

      Accross All Platforms Total Count of Mixed Genre is '1864'

      Total Count of Mixed Genre on 'Netflix' is '679'

      Total Count of Mixed Genre on 'Hulu' is '347'

      Total Count of Mixed Genre on 'Prime Video' is '1487'

      Total Count of Mixed Genre on 'Disney+' is '273'


plt.figure(figsize = (20, 5))
sns.lineplot(x = mixed_genres_data_movies['Mixed Genre'][:5], y = mixed_genres_data_movies['Netflix'][:5], color = 'red')
sns.lineplot(x = mixed_genres_data_movies['Mixed Genre'][:5], y = mixed_genres_data_movies['Hulu'][:5], color = 'lightgreen')
sns.lineplot(x = mixed_genres_data_movies['Mixed Genre'][:5], y = mixed_genres_data_movies['Prime Video'][:5], color = 'lightblue')
sns.lineplot(x = mixed_genres_data_movies['Mixed Genre'][:5], y = mixed_genres_data_movies['Disney+'][:5], color = 'darkblue')
plt.xlabel('Mixed Genre', fontsize = 15)
plt.ylabel('Movies Count', fontsize = 15)
plt.show()


fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_g_ax1 = sns.barplot(y = mixed_genres_data_movies['Mixed Genre'][:10], x = mixed_genres_data_movies['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_g_ax2 = sns.barplot(y = mixed_genres_data_movies['Mixed Genre'][:10], x = mixed_genres_data_movies['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_g_ax3 = sns.barplot(y = mixed_genres_data_movies['Mixed Genre'][:10], x = mixed_genres_data_movies['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_g_ax4 = sns.barplot(y = mixed_genres_data_movies['Mixed Genre'][:10], x = mixed_genres_data_movies['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_g_ax1.title.set_text(labels[0])
h_g_ax2.title.set_text(labels[1])
p_g_ax3.title.set_text(labels[2])
d_g_ax4.title.set_text(labels[3])
 
plt.show()


fig, axes = plt.subplots(2, 2, figsize = (20 , 10))
 
n_mg_ax1 = sns.lineplot(y = mixed_genres_data_movies['Mixed Genre'][:10], x = mixed_genres_data_movies['Netflix'][:10], color = 'red', ax = axes[0, 0])
h_mg_ax2 = sns.lineplot(y = mixed_genres_data_movies['Mixed Genre'][:10], x = mixed_genres_data_movies['Hulu'][:10], color = 'lightgreen', ax = axes[0, 1])
p_mg_ax3 = sns.lineplot(y = mixed_genres_data_movies['Mixed Genre'][:10], x = mixed_genres_data_movies['Prime Video'][:10], color = 'lightblue', ax = axes[1, 0])
d_mg_ax4 = sns.lineplot(y = mixed_genres_data_movies['Mixed Genre'][:10], x = mixed_genres_data_movies['Disney+'][:10], color = 'darkblue', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_mg_ax1.title.set_text(labels[0])
h_mg_ax2.title.set_text(labels[1])
p_mg_ax3.title.set_text(labels[2])
d_mg_ax4.title.set_text(labels[3])
 
plt.show()


# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Mixed Genre  Movies Count Per Platform')
 
# Plotting the information from each dataset into a histogram
sns.kdeplot(netflix_mixed_genres_movies['Netflix'][:50], color = 'red', legend = True)
sns.kdeplot(hulu_mixed_genres_movies['Hulu'][:50], color = 'green', legend = True)
sns.kdeplot(prime_video_mixed_genres_movies['Prime Video'][:50], color = 'lightblue', legend = True)
sns.kdeplot(disney_mixed_genres_movies['Disney+'][:50], color = 'darkblue', legend = True)                                      

# Setting the legend
plt.legend(['Netflix', 'Hulu', 'Prime Video', 'Disney+'])
plt.show()


fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_mg_ax1 = sns.barplot(y = netflix_mixed_genres_movies['Mixed Genre'][:10], x = netflix_mixed_genres_movies['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_mg_ax2 = sns.barplot(y = hulu_mixed_genres_movies['Mixed Genre'][:10], x = hulu_mixed_genres_movies['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_mg_ax3 = sns.barplot(y = prime_video_mixed_genres_movies['Mixed Genre'][:10], x = prime_video_mixed_genres_movies['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_mg_ax4 = sns.barplot(y = disney_mixed_genres_movies['Mixed Genre'][:10], x = disney_mixed_genres_movies['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_mg_ax1.title.set_text(labels[0])
h_mg_ax2.title.set_text(labels[1])
p_mg_ax3.title.set_text(labels[2])
d_mg_ax4.title.set_text(labels[3])
 
plt.show()


fig = go.Figure(go.Funnel(y = mixed_genres_data_movies['Mixed Genre'][:10], x = mixed_genres_data_movies['Movies Count'][:10]))
fig.show()

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	Language	Plotline	Runtime	Kind	Seasons	Netflix	Prime Video
0	1	Inception	2010	13+	8.8	87%	Christopher Nolan	Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ...	Action,Adventure,Sci-Fi,Thriller	United States,United Kingdom	English,Japanese,French	Dom Cobb is a skilled thief, the absolute best...	148.0	movie	NaN	1	0
1	2	The Matrix	1999	16+	8.7	88%	Lana Wachowski,Lilly Wachowski	Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo...	Action,Sci-Fi	United States	English	Thomas A. Anderson is a man living two lives. ...	136.0	movie	NaN	1	0
2	3	Avengers: Infinity War	2018	13+	8.4	85%	Anthony Russo,Joe Russo	Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo...	Action,Adventure,Sci-Fi	United States	English	As the Avengers and their allies have continue...	149.0	movie	NaN	1	0
3	4	Back to the Future	1985	7+	8.5	96%	Robert Zemeckis	Michael J. Fox,Christopher Lloyd,Lea Thompson,...	Adventure,Comedy,Sci-Fi	United States	English	Marty McFly, a typical American teenager of th...	116.0	movie	NaN	1	0
4	5	The Good, the Bad and the Ugly	1966	16+	8.8	97%	Sergio Leone	Eli Wallach,Clint Eastwood,Lee Van Cleef,Aldo ...	Western	Italy,Spain,West Germany,United States	Italian	Blondie (The Good) (Clint Eastwood) is a profe...	161.0	movie	NaN	1	1

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	Language	Plotline	Runtime	Kind	Netflix	Prime Video	Service Provider
0	1	Inception	2010	13	8.8	87	Christopher Nolan	Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ...	Action,Adventure,Sci-Fi,Thriller	United States,United Kingdom	English,Japanese,French	Dom Cobb is a skilled thief, the absolute best...	148	movie	1	0	Netflix
1	2	The Matrix	1999	16	8.7	88	Lana Wachowski,Lilly Wachowski	Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo...	Action,Sci-Fi	United States	English	Thomas A. Anderson is a man living two lives. ...	136	movie	1	0	Netflix
2	3	Avengers: Infinity War	2018	13	8.4	85	Anthony Russo,Joe Russo	Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo...	Action,Adventure,Sci-Fi	United States	English	As the Avengers and their allies have continue...	149	movie	1	0	Netflix
3	4	Back to the Future	1985	7	8.5	96	Robert Zemeckis	Michael J. Fox,Christopher Lloyd,Lea Thompson,...	Adventure,Comedy,Sci-Fi	United States	English	Marty McFly, a typical American teenager of th...	116	movie	1	0	Netflix
4	5	The Good, the Bad and the Ugly	1966	16	8.8	97	Sergio Leone	Eli Wallach,Clint Eastwood,Lee Van Cleef,Aldo ...	Western	Italy,Spain,West Germany,United States	Italian	Blondie (The Good) (Clint Eastwood) is a profe...	161	movie	1	1	Netflix

	ID	Year	Netflix	Hulu	Prime Video	Disney+	Type
count	16923.000000	16923.000000	16923.000000	16923.000000	16923.000000	16923.000000	16923.0
mean	8462.000000	2003.211901	0.214915	0.062637	0.727235	0.033150	0.0
std	4885.393638	20.526532	0.410775	0.242315	0.445394	0.179034	0.0
min	1.000000	1901.000000	0.000000	0.000000	0.000000	0.000000	0.0
25%	4231.500000	2001.000000	0.000000	0.000000	0.000000	0.000000	0.0
50%	8462.000000	2012.000000	0.000000	0.000000	1.000000	0.000000	0.0
75%	12692.500000	2016.000000	0.000000	0.000000	1.000000	0.000000	0.0
max	16923.000000	2020.000000	1.000000	1.000000	1.000000	1.000000	0.0

	ID	Year	Netflix	Hulu	Prime Video	Disney+	Type
ID	1.000000	-0.217816	-0.644470	-0.129926	0.469301	0.263530	NaN
Year	-0.217816	1.000000	0.256151	0.101337	-0.255578	-0.047258	NaN
Netflix	-0.644470	0.256151	1.000000	-0.118032	-0.745141	-0.089649	NaN
Hulu	-0.129926	0.101337	-0.118032	1.000000	-0.284654	-0.039693	NaN
Prime Video	0.469301	-0.255578	-0.745141	-0.284654	1.000000	-0.289008	NaN
Disney+	0.263530	-0.047258	-0.089649	-0.039693	-0.289008	1.000000	NaN
Type	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	Genre	Movies Count	Netflix	Hulu	Prime Video	Disney+
7	Drama	7381	1592	511	5436	150
4	Comedy	4718	1351	308	3012	284
23	Thriller	3498	717	264	2660	23
0	Action	2801	617	158	2058	99
6	Documentary	2556	515	152	1881	65
18	Romance	2476	595	180	1731	88
12	Horror	2266	287	139	1903	15
5	Crime	1963	444	122	1462	24
1	Adventure	1849	358	133	1181	262
8	Family	1513	314	120	720	414

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Prime Video	Service Provider	Number of Genres
0	12791	Gappa, the Triphibian Monster	1967	7	4.3	NA	Hiroshi Noguchi	Tamio Kawaji,Yôko Yamamoto,Yûji Odaka,Kôji Wad...	Action,Adventure,Comedy,Drama,Family,Fantasy,H...	Japan	...	A frontier preacher's life is destroyed when a...	90	movie	1	Prime Video	9
1	8618	Inspector Gadget's Last Case	2002	7	4.9	NA	Michael Maliani	Maurice LaMarche,Bettina Bush,Jim Byrnes,Micha...	Animation,Action,Adventure,Comedy,Crime,Family...	Canada,United States	...	The tough gun-man Burt Sullivan (Franco Nero) ...	73	movie	1	Prime Video	8
2	7750	RWBY: Volume 1	2013	7	7.9	NA	Monty Oum,Kerry Shawcross	Jen Taylor,Shannon McCormick,Gray G. Haddock,W...	Animation,Action,Adventure,Comedy,Crime,Drama,...	United States	...	A married police captain arrives at a lake to ...	112	movie	1	Prime Video	8
3	13360	Nazis at the Center of the Earth	2012	16	3.1	NA	Joseph J. Lawson	Dominique Swain,Jake Busey,Joshua Michael Alle...	Action,Adventure,Comedy,Horror,Mystery,Sci-Fi,...	United States	...	Based On True Events. Silent No More is a cry ...	89	movie	1	Prime Video	8
4	6260	Motorama	1991	16	6.4	60	Barry Shils	Jordan Christopher Michael,Martha Quinn,Michae...	Adventure,Comedy,Crime,Drama,Fantasy,Mystery,S...	United States	...	An American nurse takes a temporary position o...	90	movie	1	Prime Video	8

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Prime Video	Service Provider	Number of Genres
0	8426	Mark Of The Witch	2014	NR	4.6	38	Tom Moore	Robert Elston,Anitra Walsh,Darryl Wells,Jack G...	Horror	United States	...	Jobless and hopeless, Rodney (Steve Olson) hit...	77	movie	1	Prime Video	1
1	8947	The Longshot	1986	13	5.5	41	Paul Bartel	Brad Trumbull,Tim Conway,Jack Weston,Harvey Ko...	Comedy	United States	...	NA	89	movie	1	Prime Video	1
2	8938	Stranded	2001	16	6	0	Deon Taylor	Priscilla Quintana,Luke Goss,Paula Patton,Will...	Thriller	United States	...	Jan. 1 1994: The Day the North American Free T...	96	movie	1	Prime Video	1
3	8935	Brace for Impact	2016	NR	4.8	NA	Michel Poulette	Kerry Condon,Sheila McCarthy,Ennis Esmer,Rafae...	Thriller	Canada	...	Successful doctor Artur Planck, his wife Clara...	88	movie	1	Prime Video	1
4	8932	Black Creek	2018	NR	4.4	NA	James Crow	Chris O'Flyng,Leah Patrick,Michael Copon,Micha...	Horror	United States	...	What comes to mind when you think of British c...	80	movie	1	Prime Video	1

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Netflix	Service Provider	Number of Genres
0	1054	WHAT DID JACK DO?	2017	16	6.5	92	David Lynch	Jack Cruz,David Lynch,Toototabon,Emily Stofle	Short,Comedy,Crime,Drama,Fantasy,Music,Mystery...	United States	...	Dressed up in a formal suit, evoking memories ...	17	movie	1	Netflix	8
1	2047	Alpha and Omega: The Legend of the Saw Tooth Cave	2014	7	5.4	NA	Richard Rich	Kate Higgins,Ben Diskin,Debi Derryberry,Mariev...	Animation,Action,Adventure,Comedy,Drama,Family...	United States,India	...	NA	45	movie	1	Netflix	8
2	288	April and the Extraordinary World	2015	7	7.3	97	Christian Desmares,Franck Ekinci	Marion Cotillard,Marc-André Grondin,Philippe K...	Animation,Adventure,Comedy,Drama,Family,Myster...	France,Canada,Belgium	...	In 1941, the world is radically different from...	105	movie	1	Netflix	8
3	1012	Gantz:O	2016	18	7.1	NA	Yasushi Kawamura,Kei'ichi Sato	Daisuke Ono,Mao Ichimichi,Tomohiro Kaku,Saori ...	Animation,Action,Drama,Fantasy,Horror,Sci-Fi,T...	Japan	...	NA	95	movie	1	Netflix	7
4	423	Om Shanti Om	2007	7	6.7	76	Farah Khan	Shah Rukh Khan,Arjun Rampal,Kirron Kher,Shreya...	Action,Comedy,Drama,Fantasy,Musical,Romance,Th...	India	...	Om Prakash Makhija is a Junior Artist in the 1...	162	movie	1	Netflix	7

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Hulu	Prime Video	Service Provider	Number of Genres
0	3828	The Osiris Child	2016	NR	5.6	62	Shane Abbess	Kellan Lutz,Daniel MacPherson,Isabel Lucas,Luk...	Action,Adventure,Drama,Fantasy,Horror,Sci-Fi,T...	Australia	...	Set in a time of interplanetary colonization, ...	99	movie	1	0	Hulu	7
1	3875	The Lorax	1972	7	6.4	54	Chris Renaud,Kyle Balda	Danny DeVito,Ed Helms,Zac Efron,Taylor Swift,B...	Animation,Adventure,Comedy,Drama,Family,Fantas...	United States,France,Japan	...	In the walled city of Thneed-Ville, where ever...	86	movie	1	0	Hulu	7
2	3549	Thelma	2017	NR	7	85	Joachim Trier	Eili Harboe,Kaya Wilkins,Henrik Rafaelsen,Elle...	Drama,Fantasy,Horror,Mystery,Romance,Sci-Fi,Th...	Norway,France,Denmark,Sweden	...	Having just enrolled at a university in Oslo a...	116	movie	1	0	Hulu	7
3	4032	Wakko's Wish	1999	0	7.3	NA	Liz Holzman,Rusty Mills,Tom Ruegger,Russell Ca...	Rob Paulsen,Jess Harnell,Tress MacNeille,Mauri...	Animation,Adventure,Comedy,Drama,Family,Fantas...	United States	...	The Warner Brothers (and the Warner Sister) go...	80	movie	1	0	Hulu	7
4	3947	Sherlock Gnomes	2018	7	5.2	27	John Stevenson	Kelly Asbury,Mary J. Blige,Emily Blunt,Julio B...	Animation,Adventure,Comedy,Family,Fantasy,Myst...	United Kingdom,United States,India,Canada,Fran...	...	Sherlock Gnomes (Johnny Depp) and his assistan...	86	movie	1	1	Prime Video	7

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Disney+	Service Provider	Number of Genres
0	15997	The Wizards Return: Alex vs. Alex	2013	7	6.5	NA	Victor Gonzalez	Selena Gomez,Jake T. Austin,Jennifer Stone,Gre...	Animation,Action,Adventure,Comedy,Drama,Family...	United States	...	Dylan Shoenfield is the pink princess of the u...	60	movie	1	Disney+	8
1	16106	Atlantis: Milo's Return	2003	0	5.1	NA	Victor Cook,Toby Shelton,Tad Stones	James Arnold Taylor,Cree Summer,John Mahoney,J...	Animation,Action,Adventure,Comedy,Drama,Family...	United States	...	NA	70	movie	1	Disney+	8
2	15932	Aladdin and the King of Thieves	1996	0	6.4	33	Tad Stones	Val Bettin,Jim Cummings,Gilbert Gottfried,Lind...	Animation,Adventure,Comedy,Family,Fantasy,Musi...	United States	...	Huckelberry was a young boy , who runs away fr...	81	movie	1	Disney+	7
3	16151	Doug's 1st Movie	1999	0	5	26	Maurice Joyce	Thomas McHugh,Fred Newman,Chris Phillips,Const...	Animation,Adventure,Comedy,Drama,Family,Fantas...	United States	...	Diving With Dolphins chronicles the making of ...	77	movie	1	Disney+	7
4	16913	Hercules	1998	7	7.3	NA	Ron Clements,John Musker	Tate Donovan,Josh Keaton,Roger Bart,Danny DeVi...	Animation,Adventure,Comedy,Family,Fantasy,Musi...	United States,France	...	NA	93	movie	1	Disney+	7

	Genre	Movies Count	Netflix	Hulu	Prime Video	Disney+
0	Talk-Show	2	0	0	2	0
1	Reality-TV	11	3	1	6	1
2	Film-Noir	81	4	2	76	0
3	News	93	6	2	83	2
4	Short	395	78	21	285	22

	Mixed Genre	Movies Count	Netflix	Hulu	Prime Video	Disney+
1004	Comedy,Drama	460	128	42	315	0
1641	Drama,Romance	418	96	37	299	1
1062	Comedy,Drama,Romance	353	110	38	219	0
1160	Comedy,Romance	298	98	23	194	1
1740	Horror,Thriller	290	38	19	243	0

	Mixed Genre	Movies Count	Netflix	Prime Video
0	Action,Adventure,Crime,Film-Noir,Romance	1	1	0
1	Drama,Musical,Sport	1	1	0
2	Drama,Musical,Thriller	1	0	1
3	Action,Adventure,Comedy,Fantasy,Sci-Fi,Thriller	1	0	1
4	Action,Adventure,Comedy,Fantasy,Western	1	0	1

	Mixed Genre	Movies Count	Netflix	Hulu	Prime Video	Disney+
0	Action,Adventure,Sci-Fi	60	12	6	33	19
1	Animation,Adventure,Comedy,Family,Fantasy	49	13	9	15	17
2	Comedy,Family	69	19	11	28	12
3	Adventure,Family	23	2	1	10	11
4	Comedy,Drama,Family	48	7	1	30	11